Hi,
I do have FPKM count and interested in dendrogram for samples cluster.
I used below code but it generate dendogram based on geneID instead of sampleID.
> countMatrix = read.table("Trinity_trans.counts.matrix.txt",header=T,sep='\t',check.names=F,row.names=1)
> dim(countMatrix)
[1] 142686 6
> head(countMatrix)
AS_0DAP AS_4DAP AS_8DAP NMK_0DAP NMK_4DAP NMK_8DAP
TRINITY_DN17944_c0_g1_i11 14.32 24.63 8.21 4.54 20 8.49
TRINITY_DN7591_c0_g1_i1 0.00 0.00 1.00 3.00 3 0.00
TRINITY_DN28918_c0_g1_i1 1.00 2.00 1.00 0.00 2 0.00
TRINITY_DN14082_c2_g2_i5 6.00 5.00 1.00 0.00 1 0.00
TRINITY_DN31994_c0_g1_i1 1.00 2.00 0.00 0.00 0 3.00
TRINITY_DN19560_c0_g1_i1 1.00 3.00 0.00 0.00 1 1.00
> rv <- rowVars(countMatrix)
> summary(rv)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000e+00 1.000e+00 1.500e+01 3.570e+05 5.180e+02 4.122e+09
> (q75 <-quantile(rowVars(countMatrix), .75))
75%
518.3202
> m2 <- countMatrix[rv >q75, ]
> dim(m2)
[1] 35672 6
> summary(rowVars(m2))
Min. 1st Qu. Median Mean 3rd Qu. Max.
5.180e+02 1.670e+03 6.677e+03 1.428e+06 4.101e+04 4.122e+09
> d <- dist(m2, method="euclidean")
> h <-hclust(d, method="complete")
> plot(h)
I will be thankful for your time and help.
Regards
nabiyogesh
Hey, you may simply have to transpose your data at some point. This can be done via the t() function in R.
thank a lot, could you please also suggest me what this value means here: 518.3202
(q75 <-quantile(rowVars(countMatrix), .75))
75%
518.3202
May I ask from where you found that line of code?
I used genefilter to use only most variable:
Oh, I see, it is just filtering out genes based on a cut-off of variance. Genes of low variance add 'no' information to the differential expression test, so, many filter these out. To put it another way: with that code, you are only retaining the genes that are in the upper quartile of the variance range.
Thanks for your all help and time.
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